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Title: Representation and Measure of Structural Information |
Abstract: We introduce a uniform representation of general objects that captures the regularities with respect to their structure. It allows a representation of a general class of objects including geometric patterns and images in a sparse, modular, hierarchical, and recursive manner. The representation can exploit any... |
Title: Adaptive optimal allocation in stratified sampling methods |
Abstract: In this paper, we propose a stratified sampling algorithm in which the random drawings made in the strata to compute the expectation of interest are also used to adaptively modify the proportion of further drawings in each stratum. These proportions converge to the optimal allocation in terms of variance redu... |
Title: Signed Chord Length Distribution. I |
Abstract: In this paper is discussed an application of signed measures (charges) to description of segment and chord length distributions in nonconvex bodies. The signed distribution may naturally appears due to definition via derivatives of nonnegative autocorrelation function simply related with distances distributio... |
Title: Circumspect descent prevails in solving random constraint satisfaction problems |
Abstract: We study the performance of stochastic local search algorithms for random instances of the $K$-satisfiability ($K$-SAT) problem. We introduce a new stochastic local search algorithm, ChainSAT, which moves in the energy landscape of a problem instance by \em never going upwards in energy. ChainSAT is a algorit... |
Title: A Method for Compressing Parameters in Bayesian Models with Application to Logistic Sequence Prediction Models |
Abstract: Bayesian classification and regression with high order interactions is largely infeasible because Markov chain Monte Carlo (MCMC) would need to be applied with a great many parameters, whose number increases rapidly with the order. In this paper we show how to make it feasible by effectively reducing the numb... |
Title: Auxiliary Information and A Priori Values in Construction of Improved Estimators |
Abstract: This volume is a collection of six papers on the use of auxiliary information and 'a priori' values in construction of improved estimators. The work included here will be of immense application for researchers and students who emply auxiliary information in any form. |
Title: On the Relationship between the Posterior and Optimal Similarity |
Abstract: For a classification problem described by the joint density $P(\omega,x)$, models of $P(\omega\eq\omega'|x,x')$ (the ``Bayesian similarity measure'') have been shown to be an optimal similarity measure for nearest neighbor classification. This paper analyzes demonstrates several additional properties of that ... |
Title: Learning Similarity for Character Recognition and 3D Object Recognition |
Abstract: I describe an approach to similarity motivated by Bayesian methods. This yields a similarity function that is learnable using a standard Bayesian methods. The relationship of the approach to variable kernel and variable metric methods is discussed. The approach is related to variable kernel Experimental resul... |
Title: Learning View Generalization Functions |
Abstract: Learning object models from views in 3D visual object recognition is usually formulated either as a function approximation problem of a function describing the view-manifold of an object, or as that of learning a class-conditional density. This paper describes an alternative framework for learning in visual o... |
Title: View Based Methods can achieve Bayes-Optimal 3D Recognition |
Abstract: This paper proves that visual object recognition systems using only 2D Euclidean similarity measurements to compare object views against previously seen views can achieve the same recognition performance as observers having access to all coordinate information and able of using arbitrary 3D models internally.... |
Title: A Spectral Approach to Analyzing Belief Propagation for 3-Coloring |
Abstract: Contributing to the rigorous understanding of BP, in this paper we relate the convergence of BP to spectral properties of the graph. This encompasses a result for random graphs with a ``planted'' solution; thus, we obtain the first rigorous result on BP for graph coloring in the case of a complex graphical st... |
Title: Summarization and Classification of Non-Poisson Point Processes |
Abstract: Fitting models for non-Poisson point processes is complicated by the lack of tractable models for much of the data. By using large samples of independent and identically distributed realizations and statistical learning, it is possible to identify absence of fit through finding a classification rule that can ... |
Title: Pac-Bayesian Supervised Classification: The Thermodynamics of Statistical Learning |
Abstract: This monograph deals with adaptive supervised classification, using tools borrowed from statistical mechanics and information theory, stemming from the PACBayesian approach pioneered by David McAllester and applied to a conception of statistical learning theory forged by Vladimir Vapnik. Using convex analysis... |
Title: Wavelet methods in statistics: Some recent developments and their applications |
Abstract: The development of wavelet theory has in recent years spawned applications in signal processing, in fast algorithms for integral transforms, and in image and function representation methods. This last application has stimulated interest in wavelet applications to statistics and to the analysis of experimental... |
Title: A Reactive Tabu Search Algorithm for Stimuli Generation in Psycholinguistics |
Abstract: The generation of meaningless "words" matching certain statistical and/or linguistic criteria is frequently needed for experimental purposes in Psycholinguistics. Such stimuli receive the name of pseudowords or nonwords in the Cognitive Neuroscience literatue. The process for building nonwords sometimes has t... |
Title: Equations of States in Singular Statistical Estimation |
Abstract: Learning machines which have hierarchical structures or hidden variables are singular statistical models because they are nonidentifiable and their Fisher information matrices are singular. In singular statistical models, neither the Bayes a posteriori distribution converges to the normal distribution nor the... |
Title: Computational Chemotaxis in Ants and Bacteria over Dynamic Environments |
Abstract: Chemotaxis can be defined as an innate behavioural response by an organism to a directional stimulus, in which bacteria, and other single-cell or multicellular organisms direct their movements according to certain chemicals in their environment. This is important for bacteria to find food (e.g., glucose) by s... |
Title: Evolving localizations in reaction-diffusion cellular automata |
Abstract: We consider hexagonal cellular automata with immediate cell neighbourhood and three cell-states. Every cell calculates its next state depending on the integral representation of states in its neighbourhood, i.e. how many neighbours are in each one state. We employ evolutionary algorithms to breed local transi... |
Title: A Universal Kernel for Learning Regular Languages |
Abstract: We give a universal kernel that renders all the regular languages linearly separable. We are not able to compute this kernel efficiently and conjecture that it is intractable, but we do have an efficient $\eps$-approximation. |
Title: Dimensionality Reduction and Reconstruction using Mirroring Neural Networks and Object Recognition based on Reduced Dimension Characteristic Vector |
Abstract: In this paper, we present a Mirroring Neural Network architecture to perform non-linear dimensionality reduction and Object Recognition using a reduced lowdimensional characteristic vector. In addition to dimensionality reduction, the network also reconstructs (mirrors) the original high-dimensional input vec... |
Title: Automatic Pattern Classification by Unsupervised Learning Using Dimensionality Reduction of Data with Mirroring Neural Networks |
Abstract: This paper proposes an unsupervised learning technique by using Multi-layer Mirroring Neural Network and Forgy's clustering algorithm. Multi-layer Mirroring Neural Network is a neural network that can be trained with generalized data inputs (different categories of image patterns) to perform non-linear dimens... |
Title: A Common View on Strong, Uniform, and Other Notions of Equivalence in Answer-Set Programming |
Abstract: Logic programming under the answer-set semantics nowadays deals with numerous different notions of program equivalence. This is due to the fact that equivalence for substitution (known as strong equivalence) and ordinary equivalence are different concepts. The former holds, given programs P and Q, iff P can b... |
Title: Kernels and Ensembles: Perspectives on Statistical Learning |
Abstract: Since their emergence in the 1990's, the support vector machine and the AdaBoost algorithm have spawned a wave of research in statistical machine learning. Much of this new research falls into one of two broad categories: kernel methods and ensemble methods. In this expository article, I discuss the main idea... |
Title: On Using Unsatisfiability for Solving Maximum Satisfiability |
Abstract: Maximum Satisfiability (MaxSAT) is a well-known optimization pro- blem, with several practical applications. The most widely known MAXS AT algorithms are ineffective at solving hard problems instances from practical application domains. Recent work proposed using efficient Boolean Satisfiability (SAT) solvers... |
Title: Cumulative and Averaging Fission of Beliefs |
Abstract: Belief fusion is the principle of combining separate beliefs or bodies of evidence originating from different sources. Depending on the situation to be modelled, different belief fusion methods can be applied. Cumulative and averaging belief fusion is defined for fusing opinions in subjective logic, and for f... |
Title: Locality and low-dimensions in the prediction of natural experience from fMRI |
Abstract: Functional Magnetic Resonance Imaging (fMRI) provides dynamical access into the complex functioning of the human brain, detailing the hemodynamic activity of thousands of voxels during hundreds of sequential time points. One approach towards illuminating the connection between fMRI and cognitive function is t... |
Title: About Algorithm for Transformation of Logic Functions (ATLF) |
Abstract: In this article the algorithm for transformation of logic functions which are given by truth tables is considered. The suggested algorithm allows the transformation of many-valued logic functions with the required number of variables and can be looked in this sense as universal. |
Title: Stochastic adaptation of importance sampler |
Abstract: Improving efficiency of importance sampler is at the center of research in Monte Carlo methods. While adaptive approach is usually difficult within the Markov Chain Monte Carlo framework, the counterpart in importance sampling can be justified and validated easily. We propose an iterative adaptation method fo... |
Title: Sequential operators in computability logic |
Abstract: Computability logic (CL) (see http://www.cis.upenn.edu/ giorgi/cl.html) is a semantical platform and research program for redeveloping logic as a formal theory of computability, as opposed to the formal theory of truth which it has more traditionally been. Formulas in CL stand for (interactive) computational ... |
Title: Population stratification using a statistical model on hypergraphs |
Abstract: Population stratification is a problem encountered in several areas of biology and public health. We tackle this problem by mapping a population and its elements attributes into a hypergraph, a natural extension of the concept of graph or network to encode associations among any number of elements. On this hy... |
Title: Reconstruction of Markov Random Fields from Samples: Some Easy Observations and Algorithms |
Abstract: Markov random fields are used to model high dimensional distributions in a number of applied areas. Much recent interest has been devoted to the reconstruction of the dependency structure from independent samples from the Markov random fields. We analyze a simple algorithm for reconstructing the underlying gr... |
Title: Ontology and Formal Semantics - Integration Overdue |
Abstract: In this note we suggest that difficulties encountered in natural language semantics are, for the most part, due to the use of mere symbol manipulation systems that are devoid of any content. In such systems, where there is hardly any link with our common-sense view of the world, and it is quite difficult to e... |
Title: Efficient blind search: Optimal power of detection under computational cost constraints |
Abstract: Some astronomy projects require a blind search through a vast number of hypotheses to detect objects of interest. The number of hypotheses to test can be in the billions. A naive blind search over every single hypothesis would be far too costly computationally. We propose a hierarchical scheme for blind searc... |
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